Corruption and money laundering: You scratch my back, i’ll scratch yours

Barone, Raffaella; Masciandaro, Donato; Schneider, Friedrich

doi:10.1111/meca.12365

Cited by 7 publications

(2 citation statements)

References 81 publications

(83 reference statements)

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“…The fact that corruption and money laundering are interrelated phenomena is revealed by previous studies (Livescu, 2017;Kyriakos-Saad, et. al., 2012;Barone, et. al., 2021).…”

Section: Discussionmentioning

confidence: 99%

Clustering of the European Union member states based on money laundering measuring indices

Gasparėnienė¹,

Gagyte²,

Remeikienė³

et al. 2022

Economics &Amp; Sociology

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The number of enforcement actions and fines for non-compliance with anti-money laundering (AML) regulations continues climbing year after year, and the year 2021 was no exception to this tendency. Globally, authorities remain harsh, and AML fines in Europe, the United States, and the United Kingdom have been increasing (Global Anti-Money Laundering Regulations, 2021). According to the UN estimations, the amount of money annually laundered worldwide amounts to 2–5% of the world’s Gross Domestic Product (GDP), or in absolute numbers - to 800 billion-2 trillion US dollars. Such high figures indicate that national governments are indeed facing a serious problem of money laundering. In this article, clustering is employed to group the EU member states by their money laundering measuring indices in order to assess the EU legal framework in terms of money laundering prevention. State clustering could help the relevant EU institutions, such as Financial Intelligence Units (FIUs), Europol, International Monetary Fund (IMF), national governments and others, to develop the most effective measures to diminish the problem of money laundering and to complement their regulatory framework. Money laundering is usually associated with criminal activities that generate large amounts of illegal financial resources. In the most general sense, money laundering refers to the process of disguising the true origin, ownership, disposal and movement of particular proceeds, property or property rights. The results of the empirical research propose that money laundering reduction calls for a higher number of suspicious transaction reports (STRs), lower levels of corruption and improvement of the legal framework in terms of money laundering prevention in the EU. The research methods cover comparative and systematic literature analysis, and hierarchical cluster analysis. The cluster analysis of the EU member states (28 countries) uses the number of reports filled with FIU between 2006-2014, the 2012-2020 Basel AML Index and the 1998-2018 CPI data.

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“…The fact that corruption and money laundering are interrelated phenomena is revealed by previous studies (Livescu, 2017;Kyriakos-Saad, et. al., 2012;Barone, et. al., 2021).…”

Section: Discussionmentioning

confidence: 99%

Clustering of the European Union member states based on money laundering measuring indices

Gasparėnienė¹,

Gagyte²,

Remeikienė³

et al. 2022

Economics &Amp; Sociology

View full text Add to dashboard Cite

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“…The first one focuses on estimating the size of the phenomenon. The second branch of literature focuses on assessing to what extent anti-money laundering (AML) policies are effective and cost-effective, a central question in ML research (see, e.g., Barone et al, 2022; Ferwerda and Reuter, 2019; Halliday et al, 2020; Harvey et al, 2021). The last branch focuses on investigating how criminals launder and integrate their illicit proceeds into the legal economy.…”

Section: Literature Reviewmentioning

confidence: 99%

Cleaning mafia cash: An empirical analysis of the money laundering behaviour of 2800 Italian criminals

Nazzari,

Riccardi

2024

European Journal of Criminology

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Despite the wide reach of anti-money laundering legislation worldwide and increasing media attention, fostered by journalistic leaks such as Panama Papers, empirical knowledge on how criminals launder their illicit proceeds is still scarce. The few available empirical studies show that money laundering (ML) schemes are often less sophisticated than they are depicted in the political and media debate. To contribute to the empirical knowledge of ML behaviour, and test this hypothesis, the present study analyses the ML activities related to 2818 Italian offenders included in the ML section of the LexisNexis’ WorldCompliance database. Through a quantitative content analysis of textual information related to each offender's profile, it highlights the characteristics of the ML offenders, the methods (or ‘typologies’ in FATF terms) employed, the assets seized, the business sectors involved and the countries in which ML was conducted. The results confirm that criminals tend to employ unsophisticated typologies, as well as prefer Italy or jurisdictions that are close (geographically and culturally) to Italy for laundering their illicit proceeds. Tangible assets (first real estate and registered vehicles) are more frequent than financial assets. Finally, differences exist between the laundering by mafia-related ML offenders and non-mafia ones. The study provides empirical ground to progress in the knowledge of how ML offenders behave, and supports the idea that criminals, when laundering their proceeds, do not act as legitimate entrepreneurs, but may be driven by other constraints and drivers.

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Money laundering, corruption and socioeconomic development in Iran: an analysis by structural equation modeling

Dell’Anno

Maddah

2023

Int Rev Econ

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This paper aims to assess the effects of money laundering (ML) and corruption on socioeconomic development in Iran as a case study for developing countries. We also investigate the complex interactions among socioeconomic development, ML, corruption, private expenditure, and dependence of the Iranian economy on oil production. The paper uses the partial least squares approach to structural equation modeling (PLS-SEM) to estimate an index of Iranian ML over the period 1997–2017. The results show that in the short run, there is a positive overall effect of ML on economic development. This effect is due to the multiplicative effect of the reinvestment of criminal proceeds in the national economy. We estimate that, in the last decade, a plausible estimate of Iranian ML is approximately 10% of official GDP. From a methodological perspective, this research applies a new calibration approach to convert the latent scores calculated by PLS-SEM into actual values of ML.

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Corruption and money laundering: You scratch my back, i’ll scratch yours

Abstract: In this paper, we develop a dynamic framework that systematically addresses the following research question: How are corruption and money laundering intertwined? We find a special relationship between these two crimes, as three different channels can be activated. First, public

Cited by 7 publications

References 81 publications

Clustering of the European Union member states based on money laundering measuring indices

Clustering of the European Union member states based on money laundering measuring indices

Cleaning mafia cash: An empirical analysis of the money laundering behaviour of 2800 Italian criminals

Money laundering, corruption and socioeconomic development in Iran: an analysis by structural equation modeling

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